jupyter_pwd = %pwd
if jupyter_pwd == "/":
%cd /workspace
# ipynb形式のライブラリのインポート
%run ./lib/lib.ipynb
# ipynb形式のライブラリノートを.py形式に変更したものをインポート
import lib
import lib.lab_lib
from lib.lab_lib import *
# 生データの入ったCSVファイルの保持されたディレクトリ名を格納している変数
csvDirPath = "./csv_files/"
# NPBのベンチマーク名のリスト
benchmarkNames = ["cg", "ep", "ft", "is", "lu", "mg"]
# NPBのプロセス数
npb_process :list[int] = [2, 4, 8, 16, 32, 64, 128, 256]
train_npb_process :list[int] = npb_process[:-1]
test_npb_process :list[int] = npb_process[-1:]
# NPBのCGの初期変数
cg_na: list[int] = [14000, 30000, 75000, 100000, 1500000]
cg_nonzer: list[int] = [11, 12, 13, 14, 15, 18, 21]
cg_niter: list[int] = [15, 30, 75, 90, 100]
cg_shift: list[int] = [20, 40, 60, 80, 110, 200]
train_cg_na: list[int] = cg_na[:-1]
train_cg_nonzer: list[int] = cg_nonzer[:-1]
train_cg_niter: list[int] = cg_niter[:-1]
train_cg_shift: list[int] = cg_shift[:-1]
test_cg_na: list[int] = cg_na[-1:]
test_cg_nonzer: list[int] = cg_nonzer[-1:]
test_cg_niter: list[int] = cg_niter[-1:]
test_cg_shift: list[int] = cg_shift[-1:]
# LULESH ベンチマークプログラムのプロセス数・問題サイズ・イテレーション数
lulesh_processes: list[int] = [8, 27, 64, 125, 216, 343, 512]
lulesh_iterations: list[int] = [8, 16, 32, 64, 128, 256]
lulesh_sizes: list[int] = [16, 24, 32, 48, 64, 128]
train_lulesh_processes: list[int] = [8, 27, 64, 125, 216, 343]
train_lulesh_iterations: list[int] = [8, 16, 32, 64, 128]
train_lulesh_sizes: list[int] = [16, 24, 32, 48]
test_lulesh_processes: list[int] = [512, 729, 1000]
test_lulesh_iterations: list[int] = [256, 512, 1024]
test_lulesh_sizes: list[int] = [64, 96, 128]
list_modelName: list[str] = [
"modelIp",
"modelLog",
"modelLinAndIp",
"modelLinAndLog",
"modelIpAndLin",
"modelIpAndLog",
"modelLogAndLin",
"modelLogAndIp",
"modelProcessDividedByProblemSize",
"modelProblemSizeDividedByProcess",
"modelInfiniteProductOfProblemSizeMultipliedByProcesses",
"modelInfiniteProductOfProblemSizeDividedByProcesses",
"modelLinearSumOf2elementCombination",
"modelLinearSumOfElementCombinations",
"modelLinearSumOf2elementCombinationWithSquared",
"modelLinearSumOf2elementCombinationWithCubed",
"modelSquareRootOfProcess",
"modelSquareRootTimesOtherElems",
"modelObeyOneParameter",
"modelLin"
# "modelBasicTree",
]
list_csvDir = [
"./csv_files/lulesh_1st/",
"./csv_files/lulesh_2nd/",
"./csv_files/lulesh_3rd/",
]
/workspace
DEBUG:__main__:hello DEBUG:lib.lab_lib:hello
list_series: list[pd.Series] = []
pis: list[str]
for elem_process in [8, 27, 64, 125, 216, 343] + [512, 729, 1000]:
print(elem_process)
fileDir: str = "./txt_files/ElapFiles/"
fileName: str = f"p{elem_process}_Elap"
with open(fileDir + fileName) as f:
l: list[str] = [s.strip() for s in f.readlines()]
if len(l) % 2 != 0:
warnings.warn("ファイルの行数が偶数ではありません")
for i in range(len(l)):
if i % 2 == 0:
pis = l[i].split(sep="/")[-3:]
else:
time = l[i].split(sep=" ")[-2]
_series: pd.Series = pd.Series(
{
"p": int(pis[0].replace("p", "")),
"i": int(pis[1].replace("i", "")),
"s": int(pis[2].replace("s", "")),
"time": float(time),
}
)
_series["cost"] = _series["p"] * _series["time"]
list_series.append(_series)
# print(f"pis={pis}, time={time}")
DF_pis_time: pd.DataFrame = pd.DataFrame(data=list_series)
DF_pis_time
8 27 64 125 216 343 512 729 1000
| p | i | s | time | cost | |
|---|---|---|---|---|---|
| 0 | 8.0 | 8.0 | 16.0 | 0.044 | 0.352 |
| 1 | 8.0 | 8.0 | 24.0 | 0.170 | 1.360 |
| 2 | 8.0 | 8.0 | 32.0 | 0.380 | 3.040 |
| 3 | 8.0 | 8.0 | 48.0 | 1.200 | 9.600 |
| 4 | 8.0 | 8.0 | 64.0 | 2.900 | 23.200 |
| ... | ... | ... | ... | ... | ... |
| 556 | 1000.0 | 512.0 | 96.0 | 980.000 | 980000.000 |
| 557 | 1000.0 | 512.0 | 128.0 | 2300.000 | 2300000.000 |
| 558 | 1000.0 | 1024.0 | 64.0 | 550.000 | 550000.000 |
| 559 | 1000.0 | 1024.0 | 96.0 | 2000.000 | 2000000.000 |
| 560 | 1000.0 | 1024.0 | 128.0 | 4700.000 | 4700000.000 |
561 rows × 5 columns
target_p_border: int = test_lulesh_processes[0]
target_i_border: int = test_lulesh_iterations[0]
target_s_border: int = test_lulesh_sizes[0]
DF_pis_time_at_train: pd.DataFrame = DF_pis_time[
(DF_pis_time["p"] < target_p_border)
& (DF_pis_time["i"] < target_i_border)
& (DF_pis_time["s"] < target_s_border)
]
DF_pis_time_at_target: pd.DataFrame = DF_pis_time[
(DF_pis_time["p"] >= target_p_border)
& (DF_pis_time["i"] >= target_i_border)
& (DF_pis_time["s"] >= target_s_border)
]
cost_to_build_model: float = sum(DF_pis_time_at_train["cost"])
DF_pis_time_at_target = DF_pis_time_at_target.sort_values(by=["p", "i", "s"])
DF_pis_time_at_target
| p | i | s | time | cost | |
|---|---|---|---|---|---|
| 480 | 512.0 | 256.0 | 64.0 | 130.0 | 66560.0 |
| 489 | 512.0 | 256.0 | 64.0 | 130.0 | 66560.0 |
| 498 | 512.0 | 256.0 | 64.0 | 130.0 | 66560.0 |
| 481 | 512.0 | 256.0 | 96.0 | 470.0 | 240640.0 |
| 490 | 512.0 | 256.0 | 96.0 | 470.0 | 240640.0 |
| ... | ... | ... | ... | ... | ... |
| 550 | 1000.0 | 1024.0 | 96.0 | 1900.0 | 1900000.0 |
| 559 | 1000.0 | 1024.0 | 96.0 | 2000.0 | 2000000.0 |
| 542 | 1000.0 | 1024.0 | 128.0 | 4600.0 | 4600000.0 |
| 551 | 1000.0 | 1024.0 | 128.0 | 4700.0 | 4700000.0 |
| 560 | 1000.0 | 1024.0 | 128.0 | 4700.0 | 4700000.0 |
81 rows × 5 columns
DF_pis_time_at_train = DF_pis_time_at_train.sort_values(by=["p", "i", "s"])
condition_col: list[str] = ["p", "i", "s"]
averaged_DF_pis_time_at_target = DF_pis_time_at_target.groupby(condition_col).aggregate(
np.mean
)
DF_pis_time_at_train
| p | i | s | time | cost | |
|---|---|---|---|---|---|
| 0 | 8.0 | 8.0 | 16.0 | 0.044 | 0.352 |
| 30 | 8.0 | 8.0 | 16.0 | 0.044 | 0.352 |
| 60 | 8.0 | 8.0 | 16.0 | 0.044 | 0.352 |
| 1 | 8.0 | 8.0 | 24.0 | 0.170 | 1.360 |
| 31 | 8.0 | 8.0 | 24.0 | 0.170 | 1.360 |
| ... | ... | ... | ... | ... | ... |
| 446 | 343.0 | 128.0 | 32.0 | 8.200 | 2812.600 |
| 476 | 343.0 | 128.0 | 32.0 | 7.500 | 2572.500 |
| 417 | 343.0 | 128.0 | 48.0 | 25.000 | 8575.000 |
| 447 | 343.0 | 128.0 | 48.0 | 27.000 | 9261.000 |
| 477 | 343.0 | 128.0 | 48.0 | 24.000 | 8232.000 |
360 rows × 5 columns
import plotly.graph_objs as go
trace_data = []
layout = go.Layout(barmode="stack")
for i, sr in DF_pis_time_at_target.reset_index().iterrows():
x = [j for j in range(1, len(DF_pis_time_at_target) + 1)]
y = [0] * len(DF_pis_time_at_target)
for k in range(int(i), len(DF_pis_time_at_target)):
y[k] = sr["cost"]
trace = go.Bar(x=x, y=y, name=f"p{int(sr['p'])}i{int(sr['i'])}s{int(sr['s'])}")
trace_data.append(trace)
fig = go.Figure(data=trace_data, layout=layout)
fig.add_shape(
type="line",
x0=0,
x1=len(DF_pis_time_at_target),
y0=cost_to_build_model,
y1=cost_to_build_model,
line=dict(
color="Red",
),
xref="x",
yref="y",
name="cost to build model",
)
fig.update_layout(
xaxis=dict(title="counts of target environments"),
yaxis=dict(title="cost"),
)
fig.show()
averaged_DF_pis_time_at_target = averaged_DF_pis_time_at_target.reset_index()
graph_x: list[int] = list(range(1, len(averaged_DF_pis_time_at_target) + 1))
graph_y: list[float] = [0] * len(averaged_DF_pis_time_at_target)
for i, sr in averaged_DF_pis_time_at_target.iterrows():
for k in range(i, len(averaged_DF_pis_time_at_target)):
graph_y[k] += sr["cost"]
data = go.Scatter(x=graph_x, y=graph_y)
fig = go.Figure(data=data)
fig.add_shape(
type="line",
x0=0,
x1=len(averaged_DF_pis_time_at_target),
y0=cost_to_build_model,
y1=cost_to_build_model,
line=dict(
color="Red",
),
xref="x",
yref="y",
name="cost to build model",
)
fig.update_layout(
xaxis=dict(title="Number of scales predicted"),
yaxis=dict(title="cost", type="log"),
width=1000,
height=500,
)
fig.show()
trace1 = go.Bar(x=["giraffes", "orangutans", "monkeys"], y=[20, 14, 23], name="SF Zoo")
trace2 = go.Bar(x=["giraffes", "orangutans", "monkeys"], y=[21, 18, 29], name="LA Zoo")
trace3 = go.Bar(x=["giraffes", "orangutans", "monkeys"], y=[22, 19, 30], name="LA Zoo")
data = [trace1, trace2, trace3]
layout = go.Layout(barmode="stack")
fig = go.Figure(data=data, layout=layout)
fig.show()
resVar = "Exclusive"
costToBuildModel: float = 0
for elem_process in train_lulesh_processes:
for elem_iteration in train_lulesh_iterations:
for elem_size in train_lulesh_sizes:
inputDFtoGetCost: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=[elem_process],
list_iteration=[elem_iteration],
list_size=[elem_size],
list_csvDir=list_csvDir,
resVar=resVar,
)
costToBuildModel += get_CostAtInputDF(
inputDF=inputDFtoGetCost,
targetColName=resVar,
numOfProcess=elem_process,
)
print(f"costToBuildModel={costToBuildModel}")
costToBuildModel=79575.00722467963
list_toDFaboutCostAndCondition: list[pd.Series] = []
for elem_process in test_lulesh_processes:
for elem_iteration in test_lulesh_iterations:
for elem_size in test_lulesh_sizes:
inputDFtoGetCost: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=[elem_process],
list_iteration=[elem_iteration],
list_size=[elem_size],
list_csvDir=list_csvDir,
resVar=resVar,
)
costInThisCondition: float = get_CostAtInputDF(
inputDF=inputDFtoGetCost,
targetColName=resVar,
numOfProcess=elem_process,
)
_series: pd.Series = pd.Series(
{
"cost": costInThisCondition,
"process": elem_process,
"iteration": elem_iteration,
"size": elem_size,
"relativeCost(=costToBuildModel/cost * 100)": costToBuildModel
/ costInThisCondition
* 100,
}
)
list_toDFaboutCostAndCondition.append(_series)
DFaboutCost: pd.DataFrame = pd.DataFrame(data=list_toDFaboutCostAndCondition)
DFaboutCost
| cost | process | iteration | size | relativeCost(=costToBuildModel/cost * 100) | |
|---|---|---|---|---|---|
| 0 | 6.766045e+04 | 512.0 | 256.0 | 64.0 | 117.609342 |
| 1 | 2.416557e+05 | 512.0 | 256.0 | 96.0 | 32.929089 |
| 2 | 5.856285e+05 | 512.0 | 256.0 | 128.0 | 13.587966 |
| 3 | 1.348294e+05 | 512.0 | 512.0 | 64.0 | 59.019038 |
| 4 | 4.893476e+05 | 512.0 | 512.0 | 96.0 | 16.261448 |
| 5 | 1.175641e+06 | 512.0 | 512.0 | 128.0 | 6.768646 |
| 6 | 2.744798e+05 | 512.0 | 1024.0 | 64.0 | 28.991209 |
| 7 | 9.680321e+05 | 512.0 | 1024.0 | 96.0 | 8.220286 |
| 8 | 2.344354e+06 | 512.0 | 1024.0 | 128.0 | 3.394325 |
| 9 | 1.042970e+05 | 729.0 | 256.0 | 64.0 | 76.296510 |
| 10 | 3.467657e+05 | 729.0 | 256.0 | 96.0 | 22.947777 |
| 11 | 8.331797e+05 | 729.0 | 256.0 | 128.0 | 9.550761 |
| 12 | 2.013598e+05 | 729.0 | 512.0 | 64.0 | 39.518824 |
| 13 | 7.011921e+05 | 729.0 | 512.0 | 96.0 | 11.348532 |
| 14 | 1.669530e+06 | 729.0 | 512.0 | 128.0 | 4.766312 |
| 15 | 3.938422e+05 | 729.0 | 1024.0 | 64.0 | 20.204793 |
| 16 | 1.400879e+06 | 729.0 | 1024.0 | 96.0 | 5.680364 |
| 17 | 3.355712e+06 | 729.0 | 1024.0 | 128.0 | 2.371330 |
| 18 | 1.400870e+05 | 1000.0 | 256.0 | 64.0 | 56.803996 |
| 19 | 4.907890e+05 | 1000.0 | 256.0 | 96.0 | 16.213689 |
| 20 | 1.160934e+06 | 1000.0 | 256.0 | 128.0 | 6.854393 |
| 21 | 2.773810e+05 | 1000.0 | 512.0 | 64.0 | 28.687982 |
| 22 | 9.880887e+05 | 1000.0 | 512.0 | 96.0 | 8.053427 |
| 23 | 2.316158e+06 | 1000.0 | 512.0 | 128.0 | 3.435647 |
| 24 | 5.627363e+05 | 1000.0 | 1024.0 | 64.0 | 14.140727 |
| 25 | 1.943521e+06 | 1000.0 | 1024.0 | 96.0 | 4.094374 |
| 26 | 4.651948e+06 | 1000.0 | 1024.0 | 128.0 | 1.710574 |
print(DFaboutCost.to_csv())
,cost,process,iteration,size,relativeCost(=costToBuildModel/cost * 100) 0,67660.44779434668,512.0,256.0,64.0,117.60934167410058 1,241655.66206907728,512.0,256.0,96.0,32.92908866415599 2,585628.5268031145,512.0,256.0,128.0,13.587966361384643 3,134829.38787379197,512.0,512.0,64.0,59.019037673868546 4,489347.61192789325,512.0,512.0,96.0,16.261447953363106 5,1175641.4546334718,512.0,512.0,128.0,6.768645909095527 6,274479.7778389333,512.0,1024.0,64.0,28.99120942577228 7,968032.1129782613,512.0,1024.0,96.0,8.220285893187782 8,2344354.329343829,512.0,1024.0,128.0,3.3943250910775173 9,104297.04679624198,729.0,256.0,64.0,76.29651046605365 10,346765.65203209495,729.0,256.0,96.0,22.947776620423337 11,833179.7265746038,729.0,256.0,128.0,9.550761340753098 12,201359.75723555396,729.0,512.0,64.0,39.518823580816836 13,701192.085256251,729.0,512.0,96.0,11.348531864217906 14,1669530.079918935,729.0,512.0,128.0,4.766311681460897 15,393842.233052451,729.0,1024.0,64.0,20.204792819687782 16,1400878.763300601,729.0,1024.0,96.0,5.680363591007226 17,3355711.696609109,729.0,1024.0,128.0,2.371330269673907 18,140086.98776966662,1000.0,256.0,64.0,56.80399621092446 19,490789.0369463333,1000.0,256.0,96.0,16.21368882234853 20,1160934.3817936662,1000.0,256.0,128.0,6.8543931916061185 21,277380.9845486667,1000.0,512.0,64.0,28.68798211029428 22,988088.7108259998,1000.0,512.0,96.0,8.053427425373409 23,2316158.048016,1000.0,512.0,128.0,3.435646686237274 24,562736.3166596666,1000.0,1024.0,64.0,14.140727169880748 25,1943520.7169620001,1000.0,1024.0,96.0,4.094374015681536 26,4651948.381511664,1000.0,1024.0,128.0,1.7105737359626827
expVar: list[str] = ["process", "iteration", "size"]
resVar: str = "Exclusive"
trainDF: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=train_lulesh_processes,
list_iteration=train_lulesh_iterations,
list_size=train_lulesh_sizes,
list_csvDir=list_csvDir,
resVar=resVar,
)
testDF: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=test_lulesh_processes,
list_iteration=test_lulesh_iterations,
list_size=test_lulesh_sizes,
list_csvDir=list_csvDir,
resVar=resVar,
)
testDF = testDF.reset_index()
trainDF = trainDF.reset_index()
testDF["functionName"] = testDF["Name"]
trainDF["functionName"] = trainDF["Name"]
functionNames: list[str] = sorted(list(set(trainDF["Name"])))
dict_symbols = {}
for elem in expVar:
dict_symbols[elem] = symbols(elem, real=True)
target_env = [
(dict_symbols["size"], test_lulesh_sizes[-1]),
(dict_symbols["iteration"], test_lulesh_iterations[-1]),
(dict_symbols["process"], test_lulesh_processes[-1]),
]
filePath: str = f"./extra-p_docker/share/input_lulesh_perFunc.txt"
dict_models: dict[str, dict[str, any]] = {}
for functionName in functionNames:
trainDF_perFunc: pd.DataFrame = trainDF[trainDF["Name"] == functionName]
testDF_perFunc: pd.DataFrame = testDF[testDF["Name"] == functionName]
# 総実行時間
resVar = "Exclusive"
# Extra-P への入力ファイル作成
str_ExtraPinputData: str = gen_ExtraPinputDataFromDF(
inputDF=testDF_perFunc,
expVar=expVar,
resVar=resVar,
)
with open(filePath, mode="w") as f:
f.write(str_ExtraPinputData)
# Extra-P の実行とその出力の取得
res_str: str = subprocess.run(
"extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
res_str_default: str = subprocess.run(
"extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt --modeler default --options poly_exponents=-1,0,1,2,3 log_exponents=0,1 force_combination_exponents=1 allow_negative_exponents=1 | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
res_str_multiParameter: str = subprocess.run(
"extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt --modeler multi-parameter | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
# 取得した Extra-P の出力の整形
res_str = res_str.replace("Model: ", "")
res_str = convert_log(res_str)
res_str_default = res_str_default.replace("Model: ", "")
res_str_default = convert_log(res_str_default)
res_str_multiParameter = res_str_multiParameter.replace("Model: ", "")
res_str_multiParameter = convert_log(res_str_multiParameter)
# 総実行時間の予測
model_sympy_all = sympify(res_str, locals=dict_symbols)
model_sympy_all_default = sympify(res_str_default, locals=dict_symbols)
model_sympy_all_multiParameter = sympify(
res_str_multiParameter, locals=dict_symbols
)
# 1コール当たりの実行時間
resVar = "ExclusivePerCall"
# Extra-P への入力ファイル作成
str_ExtraPinputData: str = gen_ExtraPinputDataFromDF(
inputDF=testDF_perFunc,
expVar=expVar,
resVar=resVar,
)
with open(filePath, mode="w") as f:
f.write(str_ExtraPinputData)
# Extra-P の実行とその出力の取得
res_str: str = subprocess.run(
"extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
res_str_default: str = subprocess.run(
"extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt --modeler default --options poly_exponents=-1,0,1,2,3 log_exponents=0,1 force_combination_exponents=1 allow_negative_exponents=1 | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
res_str_multiParameter: str = subprocess.run(
"extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt --modeler multi-parameter | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
# 取得したExtra-Pの出力の整形
res_str = res_str.replace("Model: ", "")
res_str = convert_log(res_str)
res_str_default = res_str_default.replace("Model: ", "")
res_str_default = convert_log(res_str_default)
res_str_multiParameter = res_str_multiParameter.replace("Model: ", "")
res_str_multiParameter = convert_log(res_str_multiParameter)
# 総実行時間の予測
model_sympy_perCall = sympify(res_str, locals=dict_symbols)
model_sympy_perCall_default = sympify(res_str_default, locals=dict_symbols)
model_sympy_perCall_multiParameter = sympify(
res_str_multiParameter, locals=dict_symbols
)
# 関数コール回数の予測
# 関数コール回数予測のためのモデルを構築
resVar = "#Call"
bestModelDict: dict = return_bestModelObject(
inputDF=trainDF_perFunc,
list_expVar=expVar,
list_resVar=[resVar],
list_modelName=list_modelName,
)
bestModel = bestModelDict["object"]
dict_models_perFunc: dict[str, any] = {
"time_all": model_sympy_all,
"time_all_default": model_sympy_all_default,
"time_all_multiParameter": model_sympy_all_multiParameter,
"time_perFunc": model_sympy_perCall,
"time_perFunc_default": model_sympy_perCall_default,
"time_perFunc_multiParameter": model_sympy_perCall_multiParameter,
"call_perFunc": bestModel,
}
dict_models[functionName] = dict_models_perFunc
Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated /usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning: Covariance of the parameters could not be estimated Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00]
# 集計をする
# それぞれの予測対象ケースで総実行時間の相対誤差率を算出する
# 算出された相対誤差率をプロットする
resVar: str = "Exclusive"
dict_resultDF_perFunc = {}
for elem_process in test_lulesh_processes:
for elem_iteration in test_lulesh_iterations:
for elem_size in test_lulesh_sizes:
print(
f"process={elem_process}, iteration={elem_iteration}, size={elem_size}"
)
testDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=[elem_process],
list_iteration=[elem_iteration],
list_size=[elem_size],
list_csvDir=list_csvDir,
resVar=resVar,
)
testDF_oneCase["functionName"] = testDF_oneCase["Name"]
target_env = [
(dict_symbols["size"], elem_size),
(dict_symbols["iteration"], elem_iteration),
(dict_symbols["process"], elem_process),
]
functionNames: list[str] = sorted(list(testDF_oneCase["Name"]))
list_series: list[pd.Series] = []
for functionName in functionNames:
testDF_oneCase_perFunc: pd.DataFrame = testDF_oneCase[
testDF_oneCase["Name"] == functionName
]
if len(testDF_oneCase_perFunc) != 1:
warnings.warn("len(testDF_oneCase_perFunc) != 1")
exit
# 総実行時間の予測
Exclusive_predicted_all: float = (
dict_models[functionName]["time_all"].subs(target_env).evalf()
)
Exclusive_predicted_all_default: float = (
dict_models[functionName]["time_all_default"]
.subs(target_env)
.evalf()
)
Exclusive_predicted_all_multiParameter: float = (
dict_models[functionName]["time_all_multiParameter"]
.subs(target_env)
.evalf()
)
# 1コール当たりの実行時間
Exclusive_predicted_perFunc: float = (
dict_models[functionName]["time_perFunc"].subs(target_env).evalf()
)
Exclusive_predicted_perFunc_default: float = (
dict_models[functionName]["time_perFunc_default"]
.subs(target_env)
.evalf()
)
Exclusive_predicted_perFunc_multiParameter: float = (
dict_models[functionName]["time_perFunc_multiParameter"]
.subs(target_env)
.evalf()
)
# コール回数の予測
predicted_call: float = float(
np.array(
dict_models[functionName]["call_perFunc"].predict(
inputDF=testDF_oneCase_perFunc[expVar]
)
)
)
# 実測値の取得
Exclusive_real_time: float = testDF_oneCase_perFunc.reset_index().loc[
0
][resVar]
real_call: float = testDF_oneCase_perFunc.reset_index().loc[0]["#Call"]
_series: pd.Series = pd.Series(
{
"functionName": functionName,
f"{resVar}_real_time": Exclusive_real_time,
f"{resVar}_predicted_all": Exclusive_predicted_all,
f"{resVar}_predicted_all_default": Exclusive_predicted_all_default,
f"{resVar}_predicted_all_multiParameter": Exclusive_predicted_all_multiParameter,
f"{resVar}_predicted_from_perCall": Exclusive_predicted_perFunc
* predicted_call,
f"{resVar}_predicted_from_perCall_default": Exclusive_predicted_perFunc_default
* predicted_call,
f"{resVar}_predicted_from_perCall_multiParameter": Exclusive_predicted_perFunc_multiParameter
* predicted_call,
"process": elem_process,
"iteration": elem_iteration,
"size": elem_size,
}
)
list_series.append(_series)
resultDF_perFunc: pd.DataFrame = pd.concat(list_series, axis=1).T
resultDF_perFunc = add_relativeErrorRateCol(
inputDF=resultDF_perFunc,
real_colName=f"{resVar}_real_time",
predicted_colName=f"{resVar}_predicted_all",
targetColName=f"相対誤差率(Extra-P)",
)
resultDF_perFunc = add_relativeErrorRateCol(
inputDF=resultDF_perFunc,
real_colName=f"{resVar}_real_time",
predicted_colName=f"{resVar}_predicted_from_perCall",
targetColName=f"相対誤差率(組合せ)",
)
# print(resultDF_perFunc)
dict_resultDF_perFunc[
f"p{elem_process}i{elem_iteration}s{elem_size}"
] = resultDF_perFunc
process=512, iteration=256, size=64 process=512, iteration=256, size=96 process=512, iteration=256, size=128 process=512, iteration=512, size=64 process=512, iteration=512, size=96 process=512, iteration=512, size=128 process=512, iteration=1024, size=64 process=512, iteration=1024, size=96 process=512, iteration=1024, size=128 process=729, iteration=256, size=64 process=729, iteration=256, size=96 process=729, iteration=256, size=128 process=729, iteration=512, size=64 process=729, iteration=512, size=96 process=729, iteration=512, size=128 process=729, iteration=1024, size=64 process=729, iteration=1024, size=96 process=729, iteration=1024, size=128 process=1000, iteration=256, size=64 process=1000, iteration=256, size=96 process=1000, iteration=256, size=128 process=1000, iteration=512, size=64 process=1000, iteration=512, size=96 process=1000, iteration=512, size=128 process=1000, iteration=1024, size=64 process=1000, iteration=1024, size=96 process=1000, iteration=1024, size=128
_list_series: list[pd.Series] = []
for key in dict_resultDF_perFunc.keys():
targetDF = dict_resultDF_perFunc[key]
relativeErrorOnTarget_perCall: float = ret_relativeError_fromSumOfCol(
inputDF=targetDF,
realCol="Exclusive_real_time",
predictedCol="Exclusive_predicted_from_perCall",
)
relativeErrorOnTarget_perCall_default: float = ret_relativeError_fromSumOfCol(
inputDF=targetDF,
realCol="Exclusive_real_time",
predictedCol="Exclusive_predicted_from_perCall_default",
)
relativeErrorOnTarget_perCall_multiParameter: float = (
ret_relativeError_fromSumOfCol(
inputDF=targetDF,
realCol="Exclusive_real_time",
predictedCol="Exclusive_predicted_from_perCall_multiParameter",
)
)
relativeErrorOnTarget_all: float = ret_relativeError_fromSumOfCol(
inputDF=targetDF,
realCol="Exclusive_real_time",
predictedCol="Exclusive_predicted_all",
)
relativeErrorOnTarget_all_default: float = ret_relativeError_fromSumOfCol(
inputDF=targetDF,
realCol="Exclusive_real_time",
predictedCol="Exclusive_predicted_all_default",
)
relativeErrorOnTarget_all_multiParameter: float = ret_relativeError_fromSumOfCol(
inputDF=targetDF,
realCol="Exclusive_real_time",
predictedCol="Exclusive_predicted_all_multiParameter",
)
_series = pd.Series(
{
"process": targetDF.reset_index().loc[0]["process"],
"iteration": targetDF.reset_index().loc[0]["iteration"],
"size": targetDF.reset_index().loc[0]["size"],
"Extra-Pのみ": relativeErrorOnTarget_all,
"Extra-Pのみ(default)": relativeErrorOnTarget_all_default,
"Extra-Pのみ(multi-parameter)": relativeErrorOnTarget_all_multiParameter,
"組合せ": relativeErrorOnTarget_perCall,
"組合せ(default)": relativeErrorOnTarget_perCall_default,
"組合せ(multi-parameter)": relativeErrorOnTarget_perCall_multiParameter,
"条件": key,
}
)
_list_series.append(_series)
resultDF: pd.DataFrame = pd.DataFrame(data=_list_series)
resultDF
| process | iteration | size | Extra-Pのみ | Extra-Pのみ(default) | Extra-Pのみ(multi-parameter) | 組合せ | 組合せ(default) | 組合せ(multi-parameter) | 条件 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 512 | 256 | 64 | 12.6333141263911 | 12.6333141263911 | 12.6333141263911 | 0.507208978117047 | 0.507208978117047 | 0.507208978117047 | p512i256s64 |
| 1 | 512 | 256 | 96 | 8.78762731489746 | 8.78762731489746 | 8.78762731489746 | 1.68957291981639 | 1.68957291981639 | 1.68957291981639 | p512i256s96 |
| 2 | 512 | 256 | 128 | 9.21255948992760 | 9.21255948992760 | 9.21255948992760 | 0.0990770132824140 | 0.0990770132824140 | 0.0990770132824140 | p512i256s128 |
| 3 | 512 | 512 | 64 | 13.3301888200060 | 13.3301888200060 | 13.3301888200060 | 0.160422824240916 | 0.160422824240916 | 0.160422824240916 | p512i512s64 |
| 4 | 512 | 512 | 96 | 9.53668899316149 | 9.53668899316149 | 9.53668899316149 | 0.170231071504648 | 0.170231071504648 | 0.170231071504648 | p512i512s96 |
| 5 | 512 | 512 | 128 | 8.87409086042504 | 8.87409086042504 | 8.87409086042504 | 0.425618036798869 | 0.425618036798869 | 0.425618036798869 | p512i512s128 |
| 6 | 512 | 1024 | 64 | 15.7641189708340 | 15.7641189708340 | 15.7641189708340 | 2.82949404518662 | 2.82949404518662 | 2.82949404518662 | p512i1024s64 |
| 7 | 512 | 1024 | 96 | 8.54672858981889 | 8.54672858981889 | 8.54672858981889 | 0.760886080019178 | 0.760886080019178 | 0.760886080019178 | p512i1024s96 |
| 8 | 512 | 1024 | 128 | 8.40751501760401 | 8.40751501760401 | 8.40751501760401 | 0.417010936391316 | 0.417010936391316 | 0.417010936391316 | p512i1024s128 |
| 9 | 729 | 256 | 64 | 14.3130133544459 | 14.3130133544459 | 14.3130133544459 | 3.76154927956209 | 3.76154927956209 | 3.76154927956209 | p729i256s64 |
| 10 | 729 | 256 | 96 | 7.93703415766320 | 7.93703415766320 | 7.93703415766320 | 2.02957338955345 | 2.02957338955345 | 2.02957338955345 | p729i256s96 |
| 11 | 729 | 256 | 128 | 8.46494699503024 | 8.46494699503024 | 8.46494699503024 | 0.684945221786042 | 0.684945221786042 | 0.684945221786042 | p729i256s128 |
| 12 | 729 | 512 | 64 | 14.6495731810092 | 14.6495731810092 | 14.6495731810092 | 0.957466818284560 | 0.957466818284560 | 0.957466818284560 | p729i512s64 |
| 13 | 729 | 512 | 96 | 9.26813284540945 | 9.26813284540945 | 9.26813284540945 | 0.656547350916434 | 0.656547350916434 | 0.656547350916434 | p729i512s96 |
| 14 | 729 | 512 | 128 | 8.25169349468960 | 8.25169349468960 | 8.25169349468960 | 0.340345895005132 | 0.340345895005132 | 0.340345895005132 | p729i512s128 |
| 15 | 729 | 1024 | 64 | 14.8793930637566 | 14.8793930637566 | 14.8793930637566 | 0.0817976553047962 | 0.0817976553047962 | 0.0817976553047962 | p729i1024s64 |
| 16 | 729 | 1024 | 96 | 9.52582738419136 | 9.52582738419136 | 9.52582738419136 | 0.266803086211699 | 0.266803086211699 | 0.266803086211699 | p729i1024s96 |
| 17 | 729 | 1024 | 128 | 8.65520760392545 | 8.65520760392545 | 8.65520760392545 | 0.440815082810937 | 0.440815082810937 | 0.440815082810937 | p729i1024s128 |
| 18 | 1000 | 256 | 64 | 0.406657331497498 | 0.406657331497498 | 0.406657331497498 | 4.00981476929486 | 4.00981476929486 | 4.00981476929486 | p1000i256s64 |
| 19 | 1000 | 256 | 96 | 7.27377910767353 | 7.27377910767353 | 7.27377910767353 | 0.638296016436708 | 0.638296016436708 | 0.638296016436708 | p1000i256s96 |
| 20 | 1000 | 256 | 128 | 8.38268605155095 | 8.38268605155095 | 8.38268605155095 | 0.0962293661081201 | 0.0962293661081201 | 0.0962293661081201 | p1000i256s128 |
| 21 | 1000 | 512 | 64 | 8.78484832879463 | 8.78484832879463 | 8.78484832879463 | 4.47486930123986 | 4.47486930123986 | 4.47486930123986 | p1000i512s64 |
| 22 | 1000 | 512 | 96 | 9.87695128833714 | 9.87695128833714 | 9.87695128833714 | 0.255622230013760 | 0.255622230013760 | 0.255622230013760 | p1000i512s96 |
| 23 | 1000 | 512 | 128 | 8.48722370921635 | 8.48722370921635 | 8.48722370921635 | 0.00464939710336938 | 0.00464939710336938 | 0.00464939710336938 | p1000i512s128 |
| 24 | 1000 | 1024 | 64 | 15.0909877410756 | 15.0909877410756 | 15.0909877410756 | 1.90010926607053 | 1.90010926607053 | 1.90010926607053 | p1000i1024s64 |
| 25 | 1000 | 1024 | 96 | 9.56472505537316 | 9.56472505537316 | 9.56472505537316 | 0.943611582698895 | 0.943611582698895 | 0.943611582698895 | p1000i1024s96 |
| 26 | 1000 | 1024 | 128 | 9.17669412006006 | 9.17669412006006 | 9.17669412006006 | 0.692473529391278 | 0.692473529391278 | 0.692473529391278 | p1000i1024s128 |
print(resultDF.to_csv())
,process,iteration,size,Extra-Pのみ,Extra-Pのみ(default),Extra-Pのみ(multi-parameter),組合せ,組合せ(default),組合せ(multi-parameter),条件 0,512,256,64,12.6333141263911,12.6333141263911,12.6333141263911,0.507208978117047,0.507208978117047,0.507208978117047,p512i256s64 1,512,256,96,8.78762731489746,8.78762731489746,8.78762731489746,1.68957291981639,1.68957291981639,1.68957291981639,p512i256s96 2,512,256,128,9.21255948992760,9.21255948992760,9.21255948992760,0.0990770132824140,0.0990770132824140,0.0990770132824140,p512i256s128 3,512,512,64,13.3301888200060,13.3301888200060,13.3301888200060,0.160422824240916,0.160422824240916,0.160422824240916,p512i512s64 4,512,512,96,9.53668899316149,9.53668899316149,9.53668899316149,0.170231071504648,0.170231071504648,0.170231071504648,p512i512s96 5,512,512,128,8.87409086042504,8.87409086042504,8.87409086042504,0.425618036798869,0.425618036798869,0.425618036798869,p512i512s128 6,512,1024,64,15.7641189708340,15.7641189708340,15.7641189708340,2.82949404518662,2.82949404518662,2.82949404518662,p512i1024s64 7,512,1024,96,8.54672858981889,8.54672858981889,8.54672858981889,0.760886080019178,0.760886080019178,0.760886080019178,p512i1024s96 8,512,1024,128,8.40751501760401,8.40751501760401,8.40751501760401,0.417010936391316,0.417010936391316,0.417010936391316,p512i1024s128 9,729,256,64,14.3130133544459,14.3130133544459,14.3130133544459,3.76154927956209,3.76154927956209,3.76154927956209,p729i256s64 10,729,256,96,7.93703415766320,7.93703415766320,7.93703415766320,2.02957338955345,2.02957338955345,2.02957338955345,p729i256s96 11,729,256,128,8.46494699503024,8.46494699503024,8.46494699503024,0.684945221786042,0.684945221786042,0.684945221786042,p729i256s128 12,729,512,64,14.6495731810092,14.6495731810092,14.6495731810092,0.957466818284560,0.957466818284560,0.957466818284560,p729i512s64 13,729,512,96,9.26813284540945,9.26813284540945,9.26813284540945,0.656547350916434,0.656547350916434,0.656547350916434,p729i512s96 14,729,512,128,8.25169349468960,8.25169349468960,8.25169349468960,0.340345895005132,0.340345895005132,0.340345895005132,p729i512s128 15,729,1024,64,14.8793930637566,14.8793930637566,14.8793930637566,0.0817976553047962,0.0817976553047962,0.0817976553047962,p729i1024s64 16,729,1024,96,9.52582738419136,9.52582738419136,9.52582738419136,0.266803086211699,0.266803086211699,0.266803086211699,p729i1024s96 17,729,1024,128,8.65520760392545,8.65520760392545,8.65520760392545,0.440815082810937,0.440815082810937,0.440815082810937,p729i1024s128 18,1000,256,64,0.406657331497498,0.406657331497498,0.406657331497498,4.00981476929486,4.00981476929486,4.00981476929486,p1000i256s64 19,1000,256,96,7.27377910767353,7.27377910767353,7.27377910767353,0.638296016436708,0.638296016436708,0.638296016436708,p1000i256s96 20,1000,256,128,8.38268605155095,8.38268605155095,8.38268605155095,0.0962293661081201,0.0962293661081201,0.0962293661081201,p1000i256s128 21,1000,512,64,8.78484832879463,8.78484832879463,8.78484832879463,4.47486930123986,4.47486930123986,4.47486930123986,p1000i512s64 22,1000,512,96,9.87695128833714,9.87695128833714,9.87695128833714,0.255622230013760,0.255622230013760,0.255622230013760,p1000i512s96 23,1000,512,128,8.48722370921635,8.48722370921635,8.48722370921635,0.00464939710336938,0.00464939710336938,0.00464939710336938,p1000i512s128 24,1000,1024,64,15.0909877410756,15.0909877410756,15.0909877410756,1.90010926607053,1.90010926607053,1.90010926607053,p1000i1024s64 25,1000,1024,96,9.56472505537316,9.56472505537316,9.56472505537316,0.943611582698895,0.943611582698895,0.943611582698895,p1000i1024s96 26,1000,1024,128,9.17669412006006,9.17669412006006,9.17669412006006,0.692473529391278,0.692473529391278,0.692473529391278,p1000i1024s128
filePath = f"./extra-p_docker/share/input_lulesh_AlltoAll.txt"
# 1. DFの作成
resVar: str = "Exclusive"
list_series_toMakeDF: list[pd.Series] = []
for elem_process in train_lulesh_processes:
for elem_iteration in train_lulesh_iterations:
for elem_size in train_lulesh_sizes:
trainDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=[elem_process],
list_iteration=[elem_iteration],
list_size=[elem_size],
list_csvDir=list_csvDir,
resVar=resVar,
)
execTime_inThisCase: float = sum(trainDF_oneCase[resVar])
_series: pd.Series = pd.Series(
{
"process": elem_process,
"iteration": elem_iteration,
"size": elem_size,
resVar: execTime_inThisCase,
}
)
list_series_toMakeDF.append(_series)
inputDFtoExtraP: pd.DataFrame = pd.DataFrame(list_series_toMakeDF)
# 2. Extra-Pへの入力ファイル作成
str_ExtraPinputData: str = gen_ExtraPinputDataFromDF(
inputDF=inputDFtoExtraP, expVar=expVar, resVar=resVar
)
with open(filePath, mode="w") as f:
f.write(str_ExtraPinputData)
# 3. Extra-Pの実行
res_str: str = subprocess.run(
f"extrap --text {filePath} | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
res_str_default: str = subprocess.run(
f"extrap --text {filePath} --modeler default --options poly_exponents=-1,0,1,2,3 log_exponents=0,1 force_combination_exponents=1 allow_negative_exponents=1 | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
res_str_multiParameter: str = subprocess.run(
f"extrap --text {filePath} --modeler multi-parameter | grep Model",
stdout=subprocess.PIPE,
text=True,
shell=True,
).stdout
# 4. 受け取った出力の整形
res_str = res_str.replace("Model: ", "")
res_str = convert_log(res_str)
res_str_default = res_str_default.replace("Model: ", "")
res_str_default = convert_log(res_str_default)
res_str_multiParameter = res_str_multiParameter.replace("Model: ", "")
res_str_multiParameter = convert_log(res_str_multiParameter)
print(f"res_str = {res_str}")
print(f"res_str_default = {res_str_default}")
print(f"res_str_multiParameter = {res_str_multiParameter}")
# 5. モデル化
model_sympy_whole = sympify(res_str, locals=dict_symbols)
model_sympy_whole_default = sympify(res_str_default, locals=dict_symbols)
model_sympy_whole_multiParameter = sympify(res_str_multiParameter, locals=dict_symbols)
Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00] Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00]
res_str = 9.933625875783926 + 3.977973652610428e-07 * iteration^(1) * size^(5/2) * 1/ln(2)*ln(size)^(2) + -1.113576890917575 * 1/ln(2)*ln(process)^(1) + -7.601955776753553e-07 * size^(5/2) * 1/ln(2)*ln(size)^(2) res_str_default = 9.933625875783926 + 3.977973652610428e-07 * iteration^(1) * size^(5/2) * 1/ln(2)*ln(size)^(2) + -1.113576890917575 * 1/ln(2)*ln(process)^(1) + -7.601955776753553e-07 * size^(5/2) * 1/ln(2)*ln(size)^(2) res_str_multiParameter = 9.933625875783926 + 3.977973652610428e-07 * iteration^(1) * size^(5/2) * 1/ln(2)*ln(size)^(2) + -1.113576890917575 * 1/ln(2)*ln(process)^(1) + -7.601955776753553e-07 * size^(5/2) * 1/ln(2)*ln(size)^(2)
Loading file: 100%|██████████| [00:00<00:00, Validating experiment] Generating models: | | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model. warnings.warn( Generating models: 100%|██████████| [00:00<00:00]
print(type(model_sympy_whole))
print(type(model_sympy_whole_default))
print(type(model_sympy_whole_multiParameter))
<class 'sympy.core.add.Add'> <class 'sympy.core.add.Add'> <class 'sympy.core.add.Add'>
list_series: list[pd.Series] = []
for elem_process in test_lulesh_processes:
for elem_iteration in test_lulesh_iterations:
for elem_size in test_lulesh_sizes:
print(
f"process={elem_process}, iteration={elem_iteration}, size={elem_size}"
)
condition: str = f"p{elem_process}i{elem_iteration}s{elem_size}"
# 実時間の取得
testDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=[elem_process],
list_iteration=[elem_iteration],
list_size=[elem_size],
list_csvDir=list_csvDir,
resVar=resVar,
)
exclusive_real_time: float = sum(testDF_oneCase[resVar])
target_env = [
(dict_symbols["size"], elem_size),
(dict_symbols["iteration"], elem_iteration),
(dict_symbols["process"], elem_process),
]
# 総実行時間の予測
exclusive_predicted_time: float = model_sympy_whole.subs(target_env).evalf()
exclusive_predicted_time_default: float = model_sympy_whole_default.subs(target_env).evalf()
exclusive_predicted_time_multiParameter: float = (model_sympy_whole_multiParameter.subs(target_env).evalf())
_series: pd.Series = pd.Series(
{
"exclusive_real_time": exclusive_real_time,
"exclusive_predicted_time": exclusive_predicted_time,
"exclusive_predicted_time_default": exclusive_predicted_time_default,
"exclusive_predicted_time_multiParameter": exclusive_predicted_time_multiParameter,
"条件": condition,
}
)
list_series.append(_series)
resultDF2: pd.DataFrame = pd.DataFrame(list_series)
resultDF2 = add_relativeErrorRateCol(
inputDF=resultDF2,
real_colName="exclusive_real_time",
predicted_colName="exclusive_predicted_time",
targetColName="Extra-P単体による全体予測",
)
resultDF2 = add_relativeErrorRateCol(
inputDF=resultDF2,
real_colName="exclusive_real_time",
predicted_colName="exclusive_predicted_time_default",
targetColName="Extra-P単体による全体予測(default)",
)
resultDF2 = add_relativeErrorRateCol(
inputDF=resultDF2,
real_colName="exclusive_real_time",
predicted_colName="exclusive_predicted_time_multiParameter",
targetColName="Extra-P単体による全体予測(multi-parameter)",
)
resultDF2
process=512, iteration=256, size=64 process=512, iteration=256, size=96 process=512, iteration=256, size=128 process=512, iteration=512, size=64 process=512, iteration=512, size=96 process=512, iteration=512, size=128 process=512, iteration=1024, size=64 process=512, iteration=1024, size=96 process=512, iteration=1024, size=128 process=729, iteration=256, size=64 process=729, iteration=256, size=96 process=729, iteration=256, size=128 process=729, iteration=512, size=64 process=729, iteration=512, size=96 process=729, iteration=512, size=128 process=729, iteration=1024, size=64 process=729, iteration=1024, size=96 process=729, iteration=1024, size=128 process=1000, iteration=256, size=64 process=1000, iteration=256, size=96 process=1000, iteration=256, size=128 process=1000, iteration=512, size=64 process=1000, iteration=512, size=96 process=1000, iteration=512, size=128 process=1000, iteration=1024, size=64 process=1000, iteration=1024, size=96 process=1000, iteration=1024, size=128
| exclusive_real_time | exclusive_predicted_time | exclusive_predicted_time_default | exclusive_predicted_time_multiParameter | 条件 | Extra-P単体による全体予測 | Extra-P単体による全体予測(default) | Extra-P単体による全体予測(multi-parameter) | |
|---|---|---|---|---|---|---|---|---|
| 0 | 132.149312 | 82.5581576693674 | 82.5581576693674 | 82.5581576693674 | p512i256s64 | 37.5266080780388 | 37.5266080780388 | 37.5266080780388 |
| 1 | 471.983715 | 274.231705131693 | 274.231705131693 | 274.231705131693 | p512i256s96 | 41.8980578293707 | 41.8980578293707 | 41.8980578293707 |
| 2 | 1143.805716 | 636.258741337536 | 636.258741337536 | 636.258741337536 | p512i256s128 | 44.3735302064036 | 44.3735302064036 | 44.3735302064036 |
| 3 | 263.338648 | 165.826470359938 | 165.826470359938 | 165.826470359938 | p512i512s64 | 37.0291935881498 | 37.0291935881498 | 37.0291935881498 |
| 4 | 955.757055 | 550.615148688673 | 550.615148688673 | 550.615148688673 | p512i512s96 | 42.3896327974435 | 42.3896327974435 | 42.3896327974435 |
| 5 | 2296.174716 | 1277.39203900681 | 1277.39203900681 | 1277.39203900681 | p512i512s128 | 44.3686915433420 | 44.3686915433420 | 44.3686915433420 |
| 6 | 536.093316 | 332.363095741079 | 332.363095741079 | 332.363095741079 | p512i1024s64 | 38.0027532959863 | 38.0027532959863 | 38.0027532959863 |
| 7 | 1890.687721 | 1103.38203580263 | 1103.38203580263 | 1103.38203580263 | p512i1024s96 | 41.6412333065201 | 41.6412333065201 | 41.6412333065201 |
| 8 | 4578.817049 | 2559.65863434536 | 2559.65863434536 | 2559.65863434536 | p512i1024s128 | 44.0978181335054 | 44.0978181335054 | 44.0978181335054 |
| 9 | 143.068651 | 81.9904840049816 | 81.9904840049816 | 81.9904840049816 | p729i256s64 | 42.6915098023799 | 42.6915098023799 | 42.6915098023799 |
| 10 | 475.673048 | 273.664031467307 | 273.664031467307 | 273.664031467307 | p729i256s96 | 42.4680392159481 | 42.4680392159481 | 42.4680392159481 |
| 11 | 1142.907718 | 635.691067673150 | 635.691067673150 | 635.691067673150 | p729i256s128 | 44.3794929769896 | 44.3794929769896 | 44.3794929769896 |
| 12 | 276.213659 | 165.258796695552 | 165.258796695552 | 165.258796695552 | p729i512s64 | 40.1699403867848 | 40.1699403867848 | 40.1699403867848 |
| 13 | 961.854712 | 550.047475024287 | 550.047475024287 | 550.047475024287 | p729i512s96 | 42.8138711596887 | 42.8138711596887 | 42.8138711596887 |
| 14 | 2290.164719 | 1276.82436534243 | 1276.82436534243 | 1276.82436534243 | p729i512s128 | 44.2474877493777 | 44.2474877493777 | 44.2474877493777 |
| 15 | 540.249977 | 331.795422076693 | 331.795422076693 | 331.795422076693 | p729i1024s64 | 38.5848336225292 | 38.5848336225292 | 38.5848336225292 |
| 16 | 1921.644394 | 1102.81436213825 | 1102.81436213825 | 1102.81436213825 | p729i1024s96 | 42.6109031659102 | 42.6109031659102 | 42.6109031659102 |
| 17 | 4603.171052 | 2559.09096068098 | 2559.09096068098 | 2559.09096068098 | p729i1024s128 | 44.4059120984211 | 44.4059120984211 | 44.4059120984211 |
| 18 | 140.086988 | 81.4826826083564 | 81.4826826083564 | 81.4826826083564 | p1000i256s64 | 41.8342246445247 | 41.8342246445247 | 41.8342246445247 |
| 19 | 490.789037 | 273.156230070682 | 273.156230070682 | 273.156230070682 | p1000i256s96 | 44.3434531931994 | 44.3434531931994 | 44.3434531931994 |
| 20 | 1160.934382 | 635.183266276524 | 635.183266276524 | 635.183266276524 | p1000i256s128 | 45.2868933647090 | 45.2868933647090 | 45.2868933647090 |
| 21 | 277.380985 | 164.750995298927 | 164.750995298927 | 164.750995298927 | p1000i512s64 | 40.6047982824067 | 40.6047982824067 | 40.6047982824067 |
| 22 | 988.088711 | 549.539673627662 | 549.539673627662 | 549.539673627662 | p1000i512s96 | 44.3835692477177 | 44.3835692477177 | 44.3835692477177 |
| 23 | 2316.158048 | 1276.31656394580 | 1276.31656394580 | 1276.31656394580 | p1000i512s128 | 44.8951005291249 | 44.8951005291249 | 44.8951005291249 |
| 24 | 562.736317 | 331.287620680068 | 331.287620680068 | 331.287620680068 | p1000i1024s64 | 41.1291557213599 | 41.1291557213599 | 41.1291557213599 |
| 25 | 1943.520717 | 1102.30656074162 | 1102.30656074162 | 1102.30656074162 | p1000i1024s96 | 43.2830043373717 | 43.2830043373717 | 43.2830043373717 |
| 26 | 4651.948382 | 2558.58315928435 | 2558.58315928435 | 2558.58315928435 | p1000i1024s128 | 44.9997517286953 | 44.9997517286953 | 44.9997517286953 |
mergedDF: pd.DataFrame = pd.merge(
left=resultDF,
right=resultDF2.drop(columns=["exclusive_real_time", "exclusive_predicted_time", "exclusive_predicted_time_default", "exclusive_predicted_time_multiParameter"]),
on="条件",
)
print(mergedDF.to_csv())
,process,iteration,size,Extra-Pのみ,Extra-Pのみ(default),Extra-Pのみ(multi-parameter),組合せ,組合せ(default),組合せ(multi-parameter),条件,Extra-P単体による全体予測,Extra-P単体による全体予測(default),Extra-P単体による全体予測(multi-parameter) 0,512,256,64,12.6333141263911,12.6333141263911,12.6333141263911,0.507208978117047,0.507208978117047,0.507208978117047,p512i256s64,37.5266080780388,37.5266080780388,37.5266080780388 1,512,256,96,8.78762731489746,8.78762731489746,8.78762731489746,1.68957291981639,1.68957291981639,1.68957291981639,p512i256s96,41.8980578293707,41.8980578293707,41.8980578293707 2,512,256,128,9.21255948992760,9.21255948992760,9.21255948992760,0.0990770132824140,0.0990770132824140,0.0990770132824140,p512i256s128,44.3735302064036,44.3735302064036,44.3735302064036 3,512,512,64,13.3301888200060,13.3301888200060,13.3301888200060,0.160422824240916,0.160422824240916,0.160422824240916,p512i512s64,37.0291935881498,37.0291935881498,37.0291935881498 4,512,512,96,9.53668899316149,9.53668899316149,9.53668899316149,0.170231071504648,0.170231071504648,0.170231071504648,p512i512s96,42.3896327974435,42.3896327974435,42.3896327974435 5,512,512,128,8.87409086042504,8.87409086042504,8.87409086042504,0.425618036798869,0.425618036798869,0.425618036798869,p512i512s128,44.3686915433420,44.3686915433420,44.3686915433420 6,512,1024,64,15.7641189708340,15.7641189708340,15.7641189708340,2.82949404518662,2.82949404518662,2.82949404518662,p512i1024s64,38.0027532959863,38.0027532959863,38.0027532959863 7,512,1024,96,8.54672858981889,8.54672858981889,8.54672858981889,0.760886080019178,0.760886080019178,0.760886080019178,p512i1024s96,41.6412333065201,41.6412333065201,41.6412333065201 8,512,1024,128,8.40751501760401,8.40751501760401,8.40751501760401,0.417010936391316,0.417010936391316,0.417010936391316,p512i1024s128,44.0978181335054,44.0978181335054,44.0978181335054 9,729,256,64,14.3130133544459,14.3130133544459,14.3130133544459,3.76154927956209,3.76154927956209,3.76154927956209,p729i256s64,42.6915098023799,42.6915098023799,42.6915098023799 10,729,256,96,7.93703415766320,7.93703415766320,7.93703415766320,2.02957338955345,2.02957338955345,2.02957338955345,p729i256s96,42.4680392159481,42.4680392159481,42.4680392159481 11,729,256,128,8.46494699503024,8.46494699503024,8.46494699503024,0.684945221786042,0.684945221786042,0.684945221786042,p729i256s128,44.3794929769896,44.3794929769896,44.3794929769896 12,729,512,64,14.6495731810092,14.6495731810092,14.6495731810092,0.957466818284560,0.957466818284560,0.957466818284560,p729i512s64,40.1699403867848,40.1699403867848,40.1699403867848 13,729,512,96,9.26813284540945,9.26813284540945,9.26813284540945,0.656547350916434,0.656547350916434,0.656547350916434,p729i512s96,42.8138711596887,42.8138711596887,42.8138711596887 14,729,512,128,8.25169349468960,8.25169349468960,8.25169349468960,0.340345895005132,0.340345895005132,0.340345895005132,p729i512s128,44.2474877493777,44.2474877493777,44.2474877493777 15,729,1024,64,14.8793930637566,14.8793930637566,14.8793930637566,0.0817976553047962,0.0817976553047962,0.0817976553047962,p729i1024s64,38.5848336225292,38.5848336225292,38.5848336225292 16,729,1024,96,9.52582738419136,9.52582738419136,9.52582738419136,0.266803086211699,0.266803086211699,0.266803086211699,p729i1024s96,42.6109031659102,42.6109031659102,42.6109031659102 17,729,1024,128,8.65520760392545,8.65520760392545,8.65520760392545,0.440815082810937,0.440815082810937,0.440815082810937,p729i1024s128,44.4059120984211,44.4059120984211,44.4059120984211 18,1000,256,64,0.406657331497498,0.406657331497498,0.406657331497498,4.00981476929486,4.00981476929486,4.00981476929486,p1000i256s64,41.8342246445247,41.8342246445247,41.8342246445247 19,1000,256,96,7.27377910767353,7.27377910767353,7.27377910767353,0.638296016436708,0.638296016436708,0.638296016436708,p1000i256s96,44.3434531931994,44.3434531931994,44.3434531931994 20,1000,256,128,8.38268605155095,8.38268605155095,8.38268605155095,0.0962293661081201,0.0962293661081201,0.0962293661081201,p1000i256s128,45.2868933647090,45.2868933647090,45.2868933647090 21,1000,512,64,8.78484832879463,8.78484832879463,8.78484832879463,4.47486930123986,4.47486930123986,4.47486930123986,p1000i512s64,40.6047982824067,40.6047982824067,40.6047982824067 22,1000,512,96,9.87695128833714,9.87695128833714,9.87695128833714,0.255622230013760,0.255622230013760,0.255622230013760,p1000i512s96,44.3835692477177,44.3835692477177,44.3835692477177 23,1000,512,128,8.48722370921635,8.48722370921635,8.48722370921635,0.00464939710336938,0.00464939710336938,0.00464939710336938,p1000i512s128,44.8951005291249,44.8951005291249,44.8951005291249 24,1000,1024,64,15.0909877410756,15.0909877410756,15.0909877410756,1.90010926607053,1.90010926607053,1.90010926607053,p1000i1024s64,41.1291557213599,41.1291557213599,41.1291557213599 25,1000,1024,96,9.56472505537316,9.56472505537316,9.56472505537316,0.943611582698895,0.943611582698895,0.943611582698895,p1000i1024s96,43.2830043373717,43.2830043373717,43.2830043373717 26,1000,1024,128,9.17669412006006,9.17669412006006,9.17669412006006,0.692473529391278,0.692473529391278,0.692473529391278,p1000i1024s128,44.9997517286953,44.9997517286953,44.9997517286953
mergedDF
| process | iteration | size | Extra-Pのみ | Extra-Pのみ(default) | Extra-Pのみ(multi-parameter) | 組合せ | 組合せ(default) | 組合せ(multi-parameter) | 条件 | Extra-P単体による全体予測 | Extra-P単体による全体予測(default) | Extra-P単体による全体予測(multi-parameter) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 512 | 256 | 64 | 12.6333141263911 | 12.6333141263911 | 12.6333141263911 | 0.507208978117047 | 0.507208978117047 | 0.507208978117047 | p512i256s64 | 37.5266080780388 | 37.5266080780388 | 37.5266080780388 |
| 1 | 512 | 256 | 96 | 8.78762731489746 | 8.78762731489746 | 8.78762731489746 | 1.68957291981639 | 1.68957291981639 | 1.68957291981639 | p512i256s96 | 41.8980578293707 | 41.8980578293707 | 41.8980578293707 |
| 2 | 512 | 256 | 128 | 9.21255948992760 | 9.21255948992760 | 9.21255948992760 | 0.0990770132824140 | 0.0990770132824140 | 0.0990770132824140 | p512i256s128 | 44.3735302064036 | 44.3735302064036 | 44.3735302064036 |
| 3 | 512 | 512 | 64 | 13.3301888200060 | 13.3301888200060 | 13.3301888200060 | 0.160422824240916 | 0.160422824240916 | 0.160422824240916 | p512i512s64 | 37.0291935881498 | 37.0291935881498 | 37.0291935881498 |
| 4 | 512 | 512 | 96 | 9.53668899316149 | 9.53668899316149 | 9.53668899316149 | 0.170231071504648 | 0.170231071504648 | 0.170231071504648 | p512i512s96 | 42.3896327974435 | 42.3896327974435 | 42.3896327974435 |
| 5 | 512 | 512 | 128 | 8.87409086042504 | 8.87409086042504 | 8.87409086042504 | 0.425618036798869 | 0.425618036798869 | 0.425618036798869 | p512i512s128 | 44.3686915433420 | 44.3686915433420 | 44.3686915433420 |
| 6 | 512 | 1024 | 64 | 15.7641189708340 | 15.7641189708340 | 15.7641189708340 | 2.82949404518662 | 2.82949404518662 | 2.82949404518662 | p512i1024s64 | 38.0027532959863 | 38.0027532959863 | 38.0027532959863 |
| 7 | 512 | 1024 | 96 | 8.54672858981889 | 8.54672858981889 | 8.54672858981889 | 0.760886080019178 | 0.760886080019178 | 0.760886080019178 | p512i1024s96 | 41.6412333065201 | 41.6412333065201 | 41.6412333065201 |
| 8 | 512 | 1024 | 128 | 8.40751501760401 | 8.40751501760401 | 8.40751501760401 | 0.417010936391316 | 0.417010936391316 | 0.417010936391316 | p512i1024s128 | 44.0978181335054 | 44.0978181335054 | 44.0978181335054 |
| 9 | 729 | 256 | 64 | 14.3130133544459 | 14.3130133544459 | 14.3130133544459 | 3.76154927956209 | 3.76154927956209 | 3.76154927956209 | p729i256s64 | 42.6915098023799 | 42.6915098023799 | 42.6915098023799 |
| 10 | 729 | 256 | 96 | 7.93703415766320 | 7.93703415766320 | 7.93703415766320 | 2.02957338955345 | 2.02957338955345 | 2.02957338955345 | p729i256s96 | 42.4680392159481 | 42.4680392159481 | 42.4680392159481 |
| 11 | 729 | 256 | 128 | 8.46494699503024 | 8.46494699503024 | 8.46494699503024 | 0.684945221786042 | 0.684945221786042 | 0.684945221786042 | p729i256s128 | 44.3794929769896 | 44.3794929769896 | 44.3794929769896 |
| 12 | 729 | 512 | 64 | 14.6495731810092 | 14.6495731810092 | 14.6495731810092 | 0.957466818284560 | 0.957466818284560 | 0.957466818284560 | p729i512s64 | 40.1699403867848 | 40.1699403867848 | 40.1699403867848 |
| 13 | 729 | 512 | 96 | 9.26813284540945 | 9.26813284540945 | 9.26813284540945 | 0.656547350916434 | 0.656547350916434 | 0.656547350916434 | p729i512s96 | 42.8138711596887 | 42.8138711596887 | 42.8138711596887 |
| 14 | 729 | 512 | 128 | 8.25169349468960 | 8.25169349468960 | 8.25169349468960 | 0.340345895005132 | 0.340345895005132 | 0.340345895005132 | p729i512s128 | 44.2474877493777 | 44.2474877493777 | 44.2474877493777 |
| 15 | 729 | 1024 | 64 | 14.8793930637566 | 14.8793930637566 | 14.8793930637566 | 0.0817976553047962 | 0.0817976553047962 | 0.0817976553047962 | p729i1024s64 | 38.5848336225292 | 38.5848336225292 | 38.5848336225292 |
| 16 | 729 | 1024 | 96 | 9.52582738419136 | 9.52582738419136 | 9.52582738419136 | 0.266803086211699 | 0.266803086211699 | 0.266803086211699 | p729i1024s96 | 42.6109031659102 | 42.6109031659102 | 42.6109031659102 |
| 17 | 729 | 1024 | 128 | 8.65520760392545 | 8.65520760392545 | 8.65520760392545 | 0.440815082810937 | 0.440815082810937 | 0.440815082810937 | p729i1024s128 | 44.4059120984211 | 44.4059120984211 | 44.4059120984211 |
| 18 | 1000 | 256 | 64 | 0.406657331497498 | 0.406657331497498 | 0.406657331497498 | 4.00981476929486 | 4.00981476929486 | 4.00981476929486 | p1000i256s64 | 41.8342246445247 | 41.8342246445247 | 41.8342246445247 |
| 19 | 1000 | 256 | 96 | 7.27377910767353 | 7.27377910767353 | 7.27377910767353 | 0.638296016436708 | 0.638296016436708 | 0.638296016436708 | p1000i256s96 | 44.3434531931994 | 44.3434531931994 | 44.3434531931994 |
| 20 | 1000 | 256 | 128 | 8.38268605155095 | 8.38268605155095 | 8.38268605155095 | 0.0962293661081201 | 0.0962293661081201 | 0.0962293661081201 | p1000i256s128 | 45.2868933647090 | 45.2868933647090 | 45.2868933647090 |
| 21 | 1000 | 512 | 64 | 8.78484832879463 | 8.78484832879463 | 8.78484832879463 | 4.47486930123986 | 4.47486930123986 | 4.47486930123986 | p1000i512s64 | 40.6047982824067 | 40.6047982824067 | 40.6047982824067 |
| 22 | 1000 | 512 | 96 | 9.87695128833714 | 9.87695128833714 | 9.87695128833714 | 0.255622230013760 | 0.255622230013760 | 0.255622230013760 | p1000i512s96 | 44.3835692477177 | 44.3835692477177 | 44.3835692477177 |
| 23 | 1000 | 512 | 128 | 8.48722370921635 | 8.48722370921635 | 8.48722370921635 | 0.00464939710336938 | 0.00464939710336938 | 0.00464939710336938 | p1000i512s128 | 44.8951005291249 | 44.8951005291249 | 44.8951005291249 |
| 24 | 1000 | 1024 | 64 | 15.0909877410756 | 15.0909877410756 | 15.0909877410756 | 1.90010926607053 | 1.90010926607053 | 1.90010926607053 | p1000i1024s64 | 41.1291557213599 | 41.1291557213599 | 41.1291557213599 |
| 25 | 1000 | 1024 | 96 | 9.56472505537316 | 9.56472505537316 | 9.56472505537316 | 0.943611582698895 | 0.943611582698895 | 0.943611582698895 | p1000i1024s96 | 43.2830043373717 | 43.2830043373717 | 43.2830043373717 |
| 26 | 1000 | 1024 | 128 | 9.17669412006006 | 9.17669412006006 | 9.17669412006006 | 0.692473529391278 | 0.692473529391278 | 0.692473529391278 | p1000i1024s128 | 44.9997517286953 | 44.9997517286953 | 44.9997517286953 |
# 実時間の取得
trainDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=[train_lulesh_processes[-1]],
list_iteration=[train_lulesh_iterations[-1]],
list_size=[train_lulesh_sizes[-1]],
list_csvDir=list_csvDir,
resVar=resVar,
)
exclusive_real_time: float = sum(trainDF_oneCase[resVar])
target_env = [
(dict_symbols["size"], train_lulesh_sizes[-1]),
(dict_symbols["iteration"], train_lulesh_iterations[-1]),
(dict_symbols["process"], train_lulesh_processes[-1]),
]
# 総実行時間の予測
exclusive_predicted_time: float = model_sympy_whole.subs(target_env).evalf()
_series: pd.Series = pd.Series(
{
"exclusive_real_time": exclusive_real_time,
"exclusive_predicted_time": exclusive_predicted_time,
"条件": condition,
}
)
_series
exclusive_real_time 26.232715 exclusive_predicted_time 17.8654504325857 条件 p1000i1024s128 dtype: object
print(train_lulesh_processes[-1])
print(train_lulesh_iterations[-1])
print(train_lulesh_sizes[-1])
343 128 48
_process = 64
_iteration = 64
_size = 24
# 実時間の取得
trainDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=[_process],
list_iteration=[_iteration],
list_size=[_size],
list_csvDir=list_csvDir,
resVar=resVar,
)
exclusive_real_time: float = sum(trainDF_oneCase[resVar])
target_env = [
(dict_symbols["size"], _size),
(dict_symbols["iteration"], _iteration),
(dict_symbols["process"], _process),
]
# 総実行時間の予測
exclusive_predicted_time: float = model_sympy_whole.subs(target_env).evalf()
_series: pd.Series = pd.Series(
{
"exclusive_real_time": exclusive_real_time,
"exclusive_predicted_time": exclusive_predicted_time,
# "条件" :condition
}
)
_series
exclusive_real_time 3.383667 exclusive_predicted_time 4.26771526403966 dtype: object
|平均絶対誤差率(%)|相対コスト(%)|
# 1. ✅モデル構築(<-上の方で作成されたモデルを使う)
# 2. ✅各条件下で1つのDFをループで回しつつ作成
# 3. ✅条件が絞られたDFで関数ごとにループを回す
# 4. ✅予測を行う
# 5. ✅目標のテーブル形式にする
resVar = "Exclusive"
_list_series: list[pd.Series] = []
for elem_process in test_lulesh_processes:
for elem_iteration in test_lulesh_iterations:
for elem_size in test_lulesh_sizes:
targetDF_oneCondition: pd.DataFrame = ret_averaged_rawDF_lulesh(
list_process=[elem_process],
list_iteration=[elem_iteration],
list_size=[elem_size],
list_csvDir=list_csvDir,
resVar=resVar,
)
for functionName in functionNames:
targetDF_oneCondition_perCall: pd.DataFrame = targetDF_oneCondition[
targetDF_oneCondition["Name"] == functionName
]
if len(targetDF_oneCondition_perCall) != 1:
warnings.warn("len(targetDF_oneCondition_perCall) != 1")
exit
predicted_call: float = float(
np.array(
dict_models[functionName]["call_perFunc"].predict(
inputDF=targetDF_oneCondition_perCall[expVar]
)
)
)
real_call: float = targetDF_oneCondition_perCall.reset_index().loc[0][
"#Call"
]
_list_series.append(
pd.Series(
{
"predicted_call": predicted_call,
"real_call": real_call,
"functionName": functionName,
"process": elem_process,
"iteration": elem_iteration,
"size": elem_size,
}
)
)
resultDF_aboutCall: pd.DataFrame = pd.DataFrame(data=_list_series)
resultDF_aboutCall = add_relativeErrorRateCol(
inputDF=resultDF_aboutCall,
real_colName="real_call",
predicted_colName="predicted_call",
targetColName="absolute_relative_error",
)
resultDF_aboutCall
| predicted_call | real_call | functionName | process | iteration | size | absolute_relative_error | |
|---|---|---|---|---|---|---|---|
| 0 | 1.000 | 1.000 | .TAU_application | 512 | 256 | 64 | 0.000000e+00 |
| 1 | 255.000 | 255.000 | MPI_Allreduce() | 512 | 256 | 64 | 0.000000e+00 |
| 2 | 1.000 | 1.000 | MPI_Barrier() | 512 | 256 | 64 | 0.000000e+00 |
| 3 | 2309.000 | 2309.000 | MPI_Comm_rank() | 512 | 256 | 64 | 3.938912e-14 |
| 4 | 1.000 | 1.000 | MPI_Comm_size() | 512 | 256 | 64 | 0.000000e+00 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 859 | 1.000 | 1.000 | void_Domain::SetupSymmetryPlanes(Int_t) | 1000 | 1024 | 128 | 0.000000e+00 |
| 860 | 1.000 | 1.000 | void_Domain::~Domain() | 1000 | 1024 | 128 | 0.000000e+00 |
| 861 | 1.000 | 1.000 | void_InitMeshDecomp(Int_t_Int_t_Int_t | 1000 | 1024 | 128 | 0.000000e+00 |
| 862 | 1.000 | 1.000 | void_ParseCommandLineOptions(int_char_**_Int_t... | 1000 | 1024 | 128 | 0.000000e+00 |
| 863 | 0.001 | 0.001 | void_VerifyAndWriteFinalOutput(Real_t_Domain | 1000 | 1024 | 128 | 6.051247e-04 |
864 rows × 7 columns
_list_series: [pd.Series] = []
for elem_process in test_lulesh_processes:
for elem_iteration in test_lulesh_iterations:
for elem_size in test_lulesh_sizes:
resultDF_aboutCall_oneCondition: pd.DataFrame = resultDF_aboutCall[
(resultDF_aboutCall["process"] == elem_process)
& (resultDF_aboutCall["iteration"] == elem_iteration)
& (resultDF_aboutCall["size"] == elem_size)
]
_list_series.append(
pd.Series(
{
"process": elem_process,
"iteration": elem_iteration,
"size": elem_size,
"MAPE": resultDF_aboutCall_oneCondition.mean()[
"absolute_relative_error"
],
}
)
)
resultDF_aboutCall_withMAPE: pd.DataFrame = pd.DataFrame(data=_list_series)
resultDF_aboutCall_withMAPE
/tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. /tmp/ipykernel_11192/3753474081.py:16: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
| process | iteration | size | MAPE | |
|---|---|---|---|---|
| 0 | 512.0 | 256.0 | 64.0 | 0.993523 |
| 1 | 512.0 | 256.0 | 96.0 | 1.001713 |
| 2 | 512.0 | 256.0 | 128.0 | 1.011022 |
| 3 | 512.0 | 512.0 | 64.0 | 1.097014 |
| 4 | 512.0 | 512.0 | 96.0 | 1.015928 |
| 5 | 512.0 | 512.0 | 128.0 | 0.987108 |
| 6 | 512.0 | 1024.0 | 64.0 | 1.519404 |
| 7 | 512.0 | 1024.0 | 96.0 | 1.207243 |
| 8 | 512.0 | 1024.0 | 128.0 | 1.096240 |
| 9 | 729.0 | 256.0 | 64.0 | 2.094636 |
| 10 | 729.0 | 256.0 | 96.0 | 2.083630 |
| 11 | 729.0 | 256.0 | 128.0 | 2.097768 |
| 12 | 729.0 | 512.0 | 64.0 | 2.188117 |
| 13 | 729.0 | 512.0 | 96.0 | 2.101251 |
| 14 | 729.0 | 512.0 | 128.0 | 2.070297 |
| 15 | 729.0 | 1024.0 | 64.0 | 2.598912 |
| 16 | 729.0 | 1024.0 | 96.0 | 2.289654 |
| 17 | 729.0 | 1024.0 | 128.0 | 2.178994 |
| 18 | 1000.0 | 256.0 | 64.0 | 3.561134 |
| 19 | 1000.0 | 256.0 | 96.0 | 3.486965 |
| 20 | 1000.0 | 256.0 | 128.0 | 3.509156 |
| 21 | 1000.0 | 512.0 | 64.0 | 3.627803 |
| 22 | 1000.0 | 512.0 | 96.0 | 3.524894 |
| 23 | 1000.0 | 512.0 | 128.0 | 3.488279 |
| 24 | 1000.0 | 1024.0 | 64.0 | 4.016989 |
| 25 | 1000.0 | 1024.0 | 96.0 | 3.706513 |
| 26 | 1000.0 | 1024.0 | 128.0 | 3.594678 |
DF_pis_time_at_target
| p | i | s | time | cost | |
|---|---|---|---|---|---|
| 480 | 512.0 | 256.0 | 64.0 | 130.0 | 66560.0 |
| 489 | 512.0 | 256.0 | 64.0 | 130.0 | 66560.0 |
| 498 | 512.0 | 256.0 | 64.0 | 130.0 | 66560.0 |
| 481 | 512.0 | 256.0 | 96.0 | 470.0 | 240640.0 |
| 490 | 512.0 | 256.0 | 96.0 | 470.0 | 240640.0 |
| ... | ... | ... | ... | ... | ... |
| 550 | 1000.0 | 1024.0 | 96.0 | 1900.0 | 1900000.0 |
| 559 | 1000.0 | 1024.0 | 96.0 | 2000.0 | 2000000.0 |
| 542 | 1000.0 | 1024.0 | 128.0 | 4600.0 | 4600000.0 |
| 551 | 1000.0 | 1024.0 | 128.0 | 4700.0 | 4700000.0 |
| 560 | 1000.0 | 1024.0 | 128.0 | 4700.0 | 4700000.0 |
81 rows × 5 columns
# resultDF_aboutCall_withMAPE
# DF_pis_time_at_target
objective_DF: pd.DataFrame = pd.merge(
resultDF_aboutCall_withMAPE,
DF_pis_time_at_target,
how="outer",
left_on=["process", "iteration", "size"],
right_on=["p", "i", "s"],
).drop(columns=["p", "i", "s", "time"])
objective_DF["relative_cost"] = costToBuildModel / objective_DF["cost"]
objective_DF = objective_DF.drop(columns=["cost"])
condition_col: list[str] = ["process", "iteration", "size"]
objective_DF = objective_DF.groupby(condition_col).aggregate(np.mean)
objective_DF
| MAPE | relative_cost | |||
|---|---|---|---|---|
| process | iteration | size | ||
| 512.0 | 256.0 | 64.0 | 0.993523 | 1.195538 |
| 96.0 | 1.001713 | 0.330681 | ||
| 128.0 | 1.011022 | 0.141291 | ||
| 512.0 | 64.0 | 1.097014 | 0.590389 | |
| 96.0 | 1.015928 | 0.163032 | ||
| 128.0 | 0.987108 | 0.067574 | ||
| 1024.0 | 64.0 | 1.519404 | 0.289625 | |
| 96.0 | 1.207243 | 0.081800 | ||
| 128.0 | 1.096240 | 0.034037 | ||
| 729.0 | 256.0 | 64.0 | 2.094636 | 0.762362 |
| 96.0 | 2.083630 | 0.230635 | ||
| 128.0 | 2.097768 | 0.099233 | ||
| 512.0 | 64.0 | 2.188117 | 0.399470 | |
| 96.0 | 2.101251 | 0.113705 | ||
| 128.0 | 2.070297 | 0.047459 | ||
| 1024.0 | 64.0 | 2.598912 | 0.202188 | |
| 96.0 | 2.289654 | 0.057451 | ||
| 128.0 | 2.178994 | 0.023730 | ||
| 1000.0 | 256.0 | 64.0 | 3.561134 | 0.582967 |
| 96.0 | 3.486965 | 0.163526 | ||
| 128.0 | 3.509156 | 0.068322 | ||
| 512.0 | 64.0 | 3.627803 | 0.287947 | |
| 96.0 | 3.524894 | 0.080652 | ||
| 128.0 | 3.488279 | 0.034598 | ||
| 1024.0 | 64.0 | 4.016989 | 0.142128 | |
| 96.0 | 3.706513 | 0.041184 | ||
| 128.0 | 3.594678 | 0.017054 |
print(objective_DF.to_csv())
process,iteration,size,MAPE,relative_cost 512.0,256.0,64.0,0.9935228424782739,1.1955379691207877 512.0,256.0,96.0,1.0017129535328058,0.3306807148631966 512.0,256.0,128.0,1.01102184985611,0.1412908508960931 512.0,512.0,64.0,1.0970138085403747,0.5903891205534754 512.0,512.0,96.0,1.0159282298637726,0.1630318772949437 512.0,512.0,128.0,0.9871075506651344,0.06757388521117497 512.0,1024.0,64.0,1.5194043925948364,0.28962485159227097 512.0,1024.0,96.0,1.2072433542690577,0.08179996630826443 512.0,1024.0,128.0,1.0962401664788017,0.034037216254517756 729.0,256.0,64.0,2.0946356523227534,0.7623620784910629 729.0,256.0,96.0,2.0836302318077387,0.23063480433074038 729.0,256.0,128.0,2.0977683231348347,0.09923308046474577 729.0,512.0,64.0,2.1881172819131347,0.39947002850226143 729.0,512.0,96.0,2.1012511001702263,0.11370457136585453 729.0,512.0,128.0,2.0702972079954542,0.04745929935270451 729.0,1024.0,64.0,2.5989122751640394,0.20218769038554188 729.0,1024.0,96.0,2.2896535946804377,0.05745073079537913 729.0,1024.0,128.0,2.178993503376056,0.023729649676352254 1000.0,256.0,64.0,3.561134026442501,0.5829670858950888 1000.0,256.0,96.0,3.4869652443523176,0.1635257376357504 1000.0,256.0,128.0,3.5091560289523542,0.06832197589997746 1000.0,512.0,64.0,3.627803425126743,0.28794702486580537 1000.0,512.0,96.0,3.5248944816066228,0.08065219243894899 1000.0,512.0,128.0,3.4882787904946517,0.03459782922812158 1000.0,1024.0,64.0,4.016988691058634,0.1421284447790995 1000.0,1024.0,96.0,3.706513299954468,0.04118355637066753 1000.0,1024.0,128.0,3.5946778500094716,0.01705353993868404
_list_series: list[pd.Series] = []
for i, sr in trainDF.iterrows():
functionName = sr["Name"]
DF_to_predict: pd.DataFrame = pd.DataFrame(sr).T[expVar]
DF_to_predict = DF_to_predict.astype(
{"process": "int32", "iteration": "int32", "size": "int32"}
)
predicted_call: float = float(
dict_models[functionName]["call_perFunc"].predict(inputDF=DF_to_predict)
)
real_call: float = sr["#Call"]
_list_series.append(
pd.Series(
{
"process": sr["process"],
"iteration": sr["iteration"],
"size": sr["size"],
"predicted_call": predicted_call,
"real_call": real_call,
}
)
)
DF_result_lulesh_about_call: pd.DataFrame = pd.DataFrame(_list_series)
DF_result_lulesh_about_call = add_relativeErrorRateCol(
inputDF=DF_result_lulesh_about_call,
real_colName="real_call",
predicted_colName="predicted_call",
targetColName="relative_error_rate",
)
DF_result_lulesh_about_call
| process | iteration | size | predicted_call | real_call | relative_error_rate | |
|---|---|---|---|---|---|---|
| 0 | 8.0 | 8.0 | 16.0 | 1.000000 | 1.000000 | 0.000000 |
| 1 | 8.0 | 8.0 | 16.0 | 1.000000 | 1.000000 | 0.000000 |
| 2 | 8.0 | 8.0 | 16.0 | 1.000000 | 1.000000 | 0.000000 |
| 3 | 8.0 | 8.0 | 16.0 | 1.000000 | 1.000000 | 0.000000 |
| 4 | 8.0 | 8.0 | 16.0 | 8.000000 | 8.000000 | 0.000000 |
| ... | ... | ... | ... | ... | ... | ... |
| 3835 | 343.0 | 128.0 | 48.0 | 1.000000 | 1.000000 | 0.000000 |
| 3836 | 343.0 | 128.0 | 48.0 | 1.000000 | 1.000000 | 0.000000 |
| 3837 | 343.0 | 128.0 | 48.0 | 2.000000 | 2.000000 | 0.000000 |
| 3838 | 343.0 | 128.0 | 48.0 | 1.000000 | 1.000000 | 0.000000 |
| 3839 | 343.0 | 128.0 | 48.0 | 0.002915 | 0.002915 | 0.000143 |
3840 rows × 6 columns
DF_result_lulesh_about_call.mean()
process 130.500000 iteration 49.600000 size 30.000000 predicted_call 63918.435869 real_call 63915.372350 relative_error_rate 0.475022 dtype: float64
_list_series: list[pd.Series] = []
for elem_process in train_lulesh_processes:
for elem_iteration in train_lulesh_iterations:
for elem_size in train_lulesh_sizes:
for functionName in functionNames:
trainDF_oneCondition: pd.DataFrame = trainDF[
(trainDF["Name"] == functionName)
& (trainDF["process"] == elem_process)
& (trainDF["iteration"] == elem_iteration)
& (trainDF["size"] == elem_size)
]
predicted_call: float = float(
np.array(
dict_models[functionName]["call_perFunc"].predict(
inputDF=trainDF_oneCondition[expVar]
)
)
)
real_call: float = trainDF.reset_index().loc[0]["#Call"]
_list_series.append(
pd.Series(
{
"process": elem_process,
"iteration": elem_iteration,
"size": elem_size,
"functionName": functionName,
"predicted_call": predicted_call,
"real_call": real_call,
}
)
)
DF_result_lulesh_about_call: pd.DataFrame = pd.DataFrame(_list_series)
DF_result_lulesh_about_call = add_relativeErrorRateCol(
inputDF=DF_result_lulesh_about_call,
real_colName="real_call",
predicted_colName="predicted_call",
targetColName="relative_error_rate",
)
DF_result_lulesh_about_call
| process | iteration | size | functionName | predicted_call | real_call | relative_error_rate | |
|---|---|---|---|---|---|---|---|
| 0 | 8 | 8 | 16 | .TAU_application | 1.000000 | 1.0 | 0.000000 |
| 1 | 8 | 8 | 16 | MPI_Allreduce() | 7.000000 | 1.0 | 600.000000 |
| 2 | 8 | 8 | 16 | MPI_Barrier() | 1.000000 | 1.0 | 0.000000 |
| 3 | 8 | 8 | 16 | MPI_Comm_rank() | 77.000000 | 1.0 | 7600.000000 |
| 4 | 8 | 8 | 16 | MPI_Comm_size() | 1.000000 | 1.0 | 0.000000 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 3835 | 343 | 128 | 48 | void_Domain::SetupSymmetryPlanes(Int_t) | 1.000000 | 1.0 | 0.000000 |
| 3836 | 343 | 128 | 48 | void_Domain::~Domain() | 1.000000 | 1.0 | 0.000000 |
| 3837 | 343 | 128 | 48 | void_InitMeshDecomp(Int_t_Int_t_Int_t | 1.000000 | 1.0 | 0.000000 |
| 3838 | 343 | 128 | 48 | void_ParseCommandLineOptions(int_char_**_Int_t... | 1.000000 | 1.0 | 0.000000 |
| 3839 | 343 | 128 | 48 | void_VerifyAndWriteFinalOutput(Real_t_Domain | 0.002915 | 1.0 | 99.708455 |
3840 rows × 7 columns
trainDF.reset_index()
| level_0 | index | %Time | Exclusive | Inclusive | #Call | #Subrs | Name | process | iteration | size | ExclusivePerCall | functionName | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 100.0 | 3.420000e-06 | 4,647 | 1.000000 | 1.000 | .TAU_application | 8 | 8 | 16 | 3.500000e-06 | .TAU_application |
| 1 | 1 | 1 | 100.0 | 2.500000e-02 | 4,647 | 1.000000 | 100.125 | int_main(int_char_**) | 8 | 8 | 16 | 2.500000e-02 | int_main(int_char_**) |
| 2 | 2 | 2 | 84.0 | 6.192000e+00 | 3,901 | 1.000000 | 0.000 | MPI_Finalize() | 8 | 8 | 16 | 3.901000e+00 | MPI_Finalize() |
| 3 | 3 | 3 | 15.1 | 7.276667e-01 | 699 | 1.000000 | 0.000 | MPI_Init() | 8 | 8 | 16 | 6.990000e-01 | MPI_Init() |
| 4 | 4 | 4 | 0.2 | 6.000000e-03 | 11 | 8.000000 | 32768.000 | void_CalcKinematicsForElems(Domain | 8 | 8 | 16 | 7.500000e-04 | void_CalcKinematicsForElems(Domain |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3835 | 3835 | 27 | 0.0 | 5.483333e-06 | 0.00538 | 1.000000 | 0.000 | void_Domain::SetupSymmetryPlanes(Int_t) | 343 | 128 | 48 | 5.380000e-06 | void_Domain::SetupSymmetryPlanes(Int_t) |
| 3836 | 3836 | 28 | 0.0 | 1.606667e-06 | 0.00355 | 1.000000 | 2.000 | void_ParseCommandLineOptions(int_char_**_Int_t... | 343 | 128 | 48 | 1.620000e-06 | void_ParseCommandLineOptions(int_char_**_Int_t... |
| 3837 | 3837 | 29 | 0.0 | 1.896667e-06 | 0.00193 | 2.000000 | 0.000 | StrToInt | 343 | 128 | 48 | 9.650000e-07 | StrToInt |
| 3838 | 3838 | 30 | 0.0 | 5.170000e-07 | 0.000478 | 1.000000 | 0.000 | MPI_Comm_size() | 343 | 128 | 48 | 4.780000e-07 | MPI_Comm_size() |
| 3839 | 3839 | 31 | 0.0 | 2.703333e-07 | 0.000254 | 0.002915 | 0.000 | void_VerifyAndWriteFinalOutput(Real_t_Domain | 343 | 128 | 48 | 8.712206e-05 | void_VerifyAndWriteFinalOutput(Real_t_Domain |
3840 rows × 13 columns
DF_result_lulesh_about_call.mean()
/tmp/ipykernel_11192/3814533107.py:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
process 1.305000e+02 iteration 4.960000e+01 size 3.000000e+01 predicted_call 6.391537e+04 real_call 1.000000e+00 relative_error_rate 6.391443e+06 dtype: float64